Method to predict homemade explosive formulation outcomes

ABSTRACT

A method and an apparatus utilizing a digital computer programmed for the purpose to perform a virtual combination of potential source chemicals, found in a raid or a chemical cache, for the manufacture of Homemade Explosives (HMEs), so as to transform the raw inventory of found chemicals quickly and accurately into a readily understandable output predicting the various HME formulations whose manufacture was contemplated by a would-be bomb maker, along with an assessment of the relative likelihoods of each such possible HME outcome.

Note: This application is a Continuation of application Ser. No.12/455,345, by the same sole inventor, Gregory Albert Ouzounian, andclaims priority from that application's filing date of Jun. 1, 2009.Examination was by Examiner Ababacar Seck, Art Unit 2129.

FIELD OF THE INVENTION

The invention relates to the field of law enforcement andanti-terrorism, more specifically to assessment of a threat from personsseeking to produce homemade explosives.

BACKGROUND OF THE INVENTION

Law enforcement, the military and other government agencies are commonlyfaced with the challenge of trying to identify which, if any, dangeroushomemade explosives (“HMEs”) someone may be trying to formulate from agiven set of chemical ingredients, as for example those found at thescene of a crime, or a raid (the Observed Materials).

As there may be many potential precursor materials found, and there aremany explosive formulations, each with multiple primary and secondarysource materials, that a would-be bomb maker may be using, this task isboth analytically difficult and extremely time consuming to accomplish,and virtually impossible to do at the scene of the raid or seizure,except in the case of the very simplest of explosive formulations, suchas ammonium nitrate and fuel oil, which combine to form the binaryexplosive commonly referred to as ANFO. This problem has significantlyhindered the responsible soldiers' or law enforcement officers'performance of their duties in this regard, for many years. Previous tothis invention, making an assessment about what HMEs, if any, had beencontemplated by the owners of a cache of chemicals required the personseeking such an assessment to send the inventory of materials to one ofa small number of explosives experts at an agency like the FBI or BATF,where the expert would attempt to intuit, from his or her experience,what the would-be bomb makers were up to, in terms of intendedend-product explosives. So far as applicant is aware, no even moderatelycomprehensive database of all known explosives precursors existed—theclosest thing known to the applicant is a small 2-sided card handed outby the FBI, entitled “Improvised Explosives Threat Card,” listing fewerthan 20 commonly seen HME's and only their, typically two to four,primary preferred ingredients.

With the rise of terrorism and the Global War On Terror, U.S. and alliedmilitary forces, and other military and law enforcement organizations,and even emergency responders, have compelling operational reasons to beconcerned about potential HME formulations. A typical scenario mayinvolve a raid on a location or facility where the raiding forces comeacross a cache of chemicals. The ability to virtually combine thesechemical components, on the spot, and thereby rapidly and accuratelypredict what were the most likely explosive formulations, if any, beingmanufactured (e.g., TNT, TATP, ANFO etc.), would provide valuable,timely, insight and situational awareness that is not currentlyavailable.

What is needed is a method and an apparatus for performing that virtualcombination of discovered potential HME ingredients, so as to quicklyand accurately predict the various explosive formulations whosemanufacture was or may have been contemplated by the materials' users,along with an assessment of the relative likelihood of each suchpossible outcome, and to make that prediction and assessment immediatelyavailable to the user, in a readily understandable form. Preferably, itshould be possible to perform the analysis and reach those conclusionsright at the scene of the chemical cache under investigation.

SUMMARY OF THE INVENTION

This need, and others that will become apparent, are met by the currentinvention. A main object of the invention is to transform a raw list ofmaterials found to be present in a chemical cache discovered in a raidor other investigation, into a comprehensive, detailed, easilyunderstandable assessment of which homemade explosives formulations mayhave been the intended products of the persons in possession of thematerials, and the relative likelihoods of those various HMEformulations. Another object of this invention is to permit thisassessment to be performed easily, at the scene of the raid or chemicalcache under investigation if appropriate, by allowing the method to becarried out on a device sufficiently portable to be easily carried tothe location, or even be carried at all times, by a user. Another objectof this invention is to allow this assessment to be performed quickly,with results available in a matter of seconds after entry of the finalobserved material under evaluation. Another object of the invention isto permit the user to perform “what-if” tests on the chemical inventoryfound, by adding or deleting real or hypothetical chemicals to the listof observed materials, and immediately see the impact of such changes onthe relative likelihoods of various HME formulations.

The invention utilizes a digital computer, comprising user input andoutput devices, memory, and processing devices, which is programmed toperform the following operations:

-   -   (1) storing a database (the “Chemicals Database”) of chemicals,        comprising known source chemicals from which specific HMEs can        potentially be created,    -   (2) storing a database (the “HME Database”) comprising HMEs of        interest and their formulations, i.e., the source chemicals from        combinations of which each HME of interest can be made,    -   (3) permitting a user to utilize a user input device (and the        term “user input device” includes not only a keyboard, stylus,        touchpad, or other device to manually enter the data, but also        potentially a port permitting external devices such as chemical        sensors that identify, by IR spectroscopy or other methods, the        chemical species under investigation) to select from among the        chemicals included in the Chemicals Database those chemicals        actually present in a chemical cache of interest, or additional        hypothetical chemicals as to which the user wishes to see how        results would differ were they also present, and adding those        selected chemicals to an Observed Materials List (OML), which is        then stored in computer memory,    -   (4) utilizing an algorithm created for the purpose to model        probable HME formulation outcomes associated with the source        chemicals found to be present, i.e., to determine what possible        HMEs, if any, were intended to be produced from the reported        source chemicals, by comparing the Observed Materials List to        the HME Database, and calculating the relative likelihoods of        each possible HME formulation, and,    -   (5) utilizing a user output device to present the resulting        relative likelihoods of each such possible HME formulation to        the user in an easily understandable form.

These steps are carried out anew each time a new Observed Material isadded to, or a previously selected material deleted from, the OML, topermit real-time “what-if” testing of real and hypothetical combinationsof found chemicals.

It will be apparent that, with the addition of appropriate databasesstructured like the HME database, and the addition of source chemicalsto the Chemicals Database, the same invention can be advantageously usedto predict formulation outcomes for other varieties of dangerous orunlawful substances, such as chemical warfare agents, or narcotics orother illicit or unlicensed drugs, from inventories of potential sourcematerials in a chemical cache, or even perform these additionalevaluations at the same place and time, and on the same apparatus, asthe HME formulation evaluation, analyzing a single set of observedmaterials for their association with these other products as well.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram outlining the steps of the instant method,including the optional use of an Inclusion List to check whether thereis a need for further processing of a given Observed Material bydetermining whether it is included in any HME formulation of interest,and also including the optional performance-improving stratagem ofperforming the comparison between the chemical keys on the ObservedMaterials List and those listed under each HME in the HME Database, withthe former listed in descending and the latter in ascending order,resulting in fewer wasted search steps.

FIG. 1( a) shows a flow chart outlining the steps of the instant methodin simplest form, in which none of the optional computer performanceenhancing features have been incorporated.

FIG. 1( b) shows an exemplary structure of a HME Database, showing theHME source materials organized by HME formulation, and each formulationorganized by bins, each containing a primary and multiple secondarysource materials for that HME formulation.

FIG. 1( c) illustrates a specific embodiment of a HME Database for thisinvention, for an exemplary HME known as “APE.”

FIG. 2 illustrates more fully the performance-enhancing method, shown inFIG. 1 and discussed above, of conducting a comparison between lists ofunique keys representing materials on the Observed Materials List andthose representing known source chemicals in HME formulations from theHME Database, by arranging in descending key order the OML list ofsource chemical keys before making the comparison to keys, arranged inascending key order, in each HME formulation.

FIG. 3 shows a typical user output device screen, showing the HMEformulation results of applying the instant method to a given sample setof Observed Materials.

DETAILED DESCRIPTION OF THE INVENTION, INCLUDING DRAWINGS

The invention is described in more detail below:

The process requires providing a digital computer, with the requiredresources of processing devices, memory, and user input and outputdevices, that is programmed with machine instructions by which itcarries out the following steps:

First, the computer must store in memory a Chemicals Database, a largereference database of chemicals, including source chemicals known to beused in the formulation of HMEs, with each listed source chemicalrepresented by a unique key.

This Chemicals Database could conceivably comprise only the primaryingredients, or primary source chemicals, preferably used in directlyformulating the explosives; however, in a highly advantageous embodimentthe database also includes known secondary source chemicals, oralternative chemical ingredients that can, either as-is or with limitedpreliminary synthesis, be used to substitute for one or more primaryingredients in the manufacture of specific HMEs, again with each sourcechemical represented by a unique key. This proves useful, when, forexample, the would-be explosives maker has found that the primary sourcechemicals are not readily available, or has concluded that the act ofobtaining them, especially in large quantities, might itself arousesuspicion.

This, of course, results in a much larger Chemicals Database, includingknown primary and secondary source chemicals, and therefore potentiallyincluding many fairly ordinary chemicals which, again, may otherwise nothave been suspected of potentially having application in the manufactureof HMEs.

In a highly advantageous embodiment, the Chemicals Database, availableto users for choosing materials to be added to the Observed MaterialsList, also encompasses, in addition to the proper chemical name of eachsuch source chemical, other terms by which each material might beidentified by the user, including, where appropriate, trade name, CASRegistry® number assigned by the American Chemical Society, UN ID numberassigned by the United Nations Committee of Experts on the Transport ofDangerous Goods, and RTECS (Registry of Toxic Effects of ChemicalSubstances) number, as well as phonetic spellings of chemical names andtrade names, all associated with the same unique key as their respectivechemical names. Selection of any such identifier from the ChemicalsDatabase results in the addition of the selected chemical and its uniquekey to the Observed Materials List.

In another advantageous embodiment, the Chemicals Database is expandedto include additional chemicals of interest that may also be encounteredat the search site or chemical cache being investigated. This permitsthe database to be used for purposes other than just predicting HMEformulation outcomes. In one advantageous embodiment, the instant methodis integrated in a single apparatus with methods for making otherdeterminations with respect to the contents of the chemical cache,including whether some of those contents are themselves hazardousmaterials, and if so how to deal with them, quite aside from theirpossible status as HME precursors.

As noted earlier, in another advantageous embodiment, the same ChemicalsDatabase can also encompass known precursors for other substances ofconcern, such as chemical warfare agents or illegal drugs. Used inconjunction with additional databases paralleling the HME Database forsuch substances, the method can, operating in the same manner, be usedto predict formulation outcomes for those substances, based on theObserved Materials picked from an expanded Chemicals Database. Inaddition, because the HMEs themselves are dangerous chemicals, and mayalso be present in the chemical cache, with or without their sourcechemical precursors, in another advantageous embodiment, the ChemicalsDatabase also encompasses many common HMEs themselves, both to allowtheir selection as Observed Materials for purposes of HME formulationoutcomes (for those, like picric acid, that are both HMEs themselves andsource chemicals for more complex HME formulations), and to permit theirselection for purposes of another, optional program that can beinstalled on the same digital computer, that provides characteristics,blast radiuses, etc., for explosives found at the site.

Each listed chemical in the Chemicals Database is assigned a single,unique key, an identifier specific to that single chemical. That uniquekey for each chemical in the Chemicals Database could be any unique setof symbols, letters, alphanumeric characters or words, or numericvalues, so long as each is associated with one, and only one, chemicalin the Chemicals Database. In a particularly advantageous embodiment,that unique key associated with each listed chemical is an integer.

Second, the computer must also store in memory an HME Database, adatabase of materials used as source chemicals in the formulation of anyof the HMEs of interest. Each such source chemical is assigned a uniquekey, identical to the one assigned to the same source chemical when itappears in the Chemicals Database. Again, to maximize its usefulness,the HME Database also incorporates both primary source chemicals and thesecondary, alternative, source chemicals, if any, which can replace thepreferred primary source materials, either by being directly substitutedfor the primary source materials in a primary chemical process producingthe explosive, or by being transformed, in some preliminary reactionstep, into a primary source chemical, which is then used in the primarychemical process for formulation of the HME sought to be produced.

In the HME Database, the known source chemicals for each HME formulationare grouped together, and, within an HME formulation, are grouped intoseparate “bins,” with each bin containing the unique key representing aknown primary source chemical required for that formulation, along withthe unique keys representing all the secondary source chemicals thatcould be used in lieu of that primary source chemical, in that HMEformulation.

Fourth, the computer is programmed to permit a user, utilizing thecomputer's user input device, to select from among the entries in theChemicals Database, creating and storing in the computer memory a list,the Observed Materials List (OML), of materials to be analyzed. Thecomputer places each such chemical (along with its unique key, thoughthat key is itself not seen or selected by the user) on the OML. In oneadvantageous embodiment, the computer's user input device is a chemicalsensor that automatically identifies the found chemical underevaluation, and transmits that identification directly to the digitalcomputer, which then adds it, with its unique key, to the OML.

In a highly advantageous embodiment, a separate Inclusion List (“IL”) iscreated and stored in the computer memory, comprising the unique keys ofall source materials, primary and secondary, that are used in any of theHME formulations and therefore present in the HME Database, with eachkey listed only once. The following description of the comparison stepsassumes the existence of such an Inclusion List, which allows asubstantial performance improvement by avoiding the need to performrepeated searches for chemicals which aren't present in any HMEformulation. However, the process can also be carried out without theuse of such a list by simply skipping the step requiring checking a keyrepresenting a material on the Observed Materials List against the keyson the Inclusion List.

Fifth, the computer performs a comparison between each of the uniquekeys representing chemicals on the Observed Materials List (which alsoappear on the Inclusion List, if such an Inclusion List is being used),and the list of keys representing source chemicals in the HME Database,to determine which of the known HMEs have formulations that include atleast one chemical placed on the Observed Materials List, and tocalculate, using the algorithm described below, the relative likelihoodsthat the respective potential HMEs were those intended to be formulatedfrom the observed source chemicals.

Ideally, the list of chemicals selected from the Chemicals Database forinclusion on the Observed Materials List would perfectly match one, andonly one, explosive formulation identified in the HME Database. However,in the real world, some of the required source chemicals for a givenformulation may not be found, even as secondary source materials; andsome of the source chemicals found may have potential application toformulating more than one of the HMEs in the HME Database—potentiallysome having simple binary formulations, and some having more complexformulations, requiring additional source chemicals.

For example, if an explosive formulation uses picric acid as a necessaryingredient, the algorithm needs to account for the fact that a would-beexplosive maker who was not able to obtain picric acid may neverthelessbe able to use secondary ingredients to make picric acid, and then usethat synthesized picric acid in the primary chemical process. As anotherexample, the explosive TATP has as its necessary ingredients hydrogenperoxide, acetone, and sulfuric acid. Someone trying to formulate TATPmay have difficulty obtaining high grade sulfuric acid, so may insteadresort to using battery acid solution. Or, they may be unable to acquirehigh concentration hydrogen peroxide, and so may substitute the lowerstrength hydrogen peroxide that is commonly used in the beauty supplyindustry.

Thus, a critical part of the algorithm is that it must be able not onlyto rank the likelihood of the various possible formulations based onperfectly matching chemical components, but also to compensate byconsidering secondary chemicals that can, either in original form orgiven a bit of additional synthesis, be used as reasonable alternativesfor critical primary HME ingredients. It must also handle situationswhere some necessary source materials are missing altogether, even whentaking into account all known secondary source materials. Finally, itmust differentiate, in evaluating likely HME outcomes, among multipleformulations, utilizing overlapping sets of source chemicals.

In its simplest form (FIG. 1( a)), chemicals on the observed materialslist are compared with the HME formulations in the HME Database todetermine the relative likelihoods that the observed materials wereintended to be used to create specific HMEs, and the results aredisplayed for a user.

In a specific embodiment, the HME database is organized by HME (FIG. 1(b)). Each HME is in turn organized by a list of primary chemicals thatare the components of each specific HME formulation. Each primarycomponent is accompanied by a list of secondary/alternative chemicalsthat can be used in place of the primary component. The organization ofthe HME database shown in FIG. 1( b) is for illustrative purposes only,and the actual HME database can be organized in alternative ways insidethe device.

FIG. 1( b) is a diagram illustrating a simplified organization of an HMEdatabase for the present invention. The HME database 200 includes HMEformulations 202. Each HME formulation includes an HME score 204 and oneor more bins 206, each listing one primary source chemical, and anynumber of secondary/alternative source chemicals that, together, make upthe HME formulation. Each bin includes a bin flag 208. An actualimplementation of an HME database may of course differ from thatillustrated in FIG. 1( b), which is merely intended to show the elementsof an HME database. In the real database, chemicals within a bin 206will be represented by the unique chemical keys assigned in theChemicals Database.

FIG. 1( c) is a diagram illustrating a specific embodiment of an HMEdatabase for the present invention. HME database 300 includes multipleHME formulations, of which a a typical formulation, 302, for the HMEknown as APE, is shown. Chemical keys 304 define possible components ofAPE, of which there are four required, represented by bins 0 through 3(reference numeral 306). The primary source chemicals for each bin areindicated by the presence of a 1 (as at 308) in the right-hand column,opposite one of the 4 bin numbers. The chemical keys have been sortedinto ascending numerical order to implement one performance-enhancingfeature of the invention.

The comparison algorithm utilized by the computer, in one preferredembodiment, operates as follows, as illustrated by the flow chartpresented as FIG. 1:

-   -   a. Starting with the first key representing a chemical placed in        the OML, compare that OML key with the keys on the Inclusion        List (IL) (again, assuming the use of such an Inclusion List as        a performance enhancement—if an IL is not used, then, for each        new OML key, this preliminary step of checking it against an IL        is simply skipped. In another advantageous performance-enhancing        embodiment, the OML keys are, for purposes of a later comparison        step, sorted in descending order.)    -   b. If the OML key does not match any key on the Inclusion List,        proceed directly to the next sequential OML key,    -   c. If the OML key does match a key on the Inclusion List,        compare that key sequentially, in key order, to the keys        associated with each of the HME formulations within the HME        Database,    -   d. Each time a match is found between an OML key and a key        representing either a primary source chemical, or any one or        more of the secondary source chemicals, in a given HME bin        (containing a primary source chemical and any number of        secondary source chemicals), award its HME 1 point, flag that        bin within that HME formulation as being found, indicating that        no further comparisons are to be made between OML keys and any        of the remaining HME keys within that bin, and proceed to        compare the OML key with the keys in the unflagged bins in the        remaining HME formulations in the HME Database.    -   e. Continue in like manner, comparing each new OML key first to        the keys on the Inclusion List, if applicable, and then, if it        matches a key on the IL, comparing it sequentially to the keys        in unflagged bins in each HME formulation in the HME Database,        and, for each HME bin in which a match between an OML key and an        HME source chemical key is found, adding 1 point to the score of        that HME, and flagging the bin as having contained a match to an        OML chemical, so that HME bin will not be searched further for        that OML key, or for any subsequent OML key.

Note that the process does not add additional points to an HMEformulation for finding either both a primary and a secondary, ormultiple secondary, source chemicals, in an HME bin (nor does it evensearch for such additional matches, once a single match is found in agiven HME bin), so the maximum score for any HME is equal to the numberof bins of primary and secondary source chemicals associated with thatHME on the HME Database. Once a match is found between the ObservedMaterials List key and the key for a primary source chemical for an HME,the computer does not continue the search through the secondary sourcechemicals for that HME. Similarly, once a match is found between theObserved Materials List key and the key for any one secondary sourcematerial in an HME bin, the computer does not continue the searchthrough the remaining secondary source chemicals in that HME bin. Onceall the bins for an HME formulation that were not previously flagged asfound have been checked against the OML key, the search continuessequentially through the remaining HME formulations, then, with the nextOML key, sequentially through all the HME keys in unflagged HME bins,and so on until all materials on the Observed Materials List (that arealso on the Inclusion List, if used) have been checked for matches toany of the HME formulations.

FIG. 1 shows a block diagram flow chart of the process, whichincorporates the performance enhancements of using an Inclusion list,stopping searching further within an HME bin when any one match has beenfound to an OML key in that bin, flagging a bin in which a match isfound so that it will not be subject to future searching, and invertingthe keys in the OML so that, in searching HME formulations, the bottomof the list for an HME formulation moves up each time a match is foundto a key under that formulation, or when the OML key is less than thecurrent key within the HME under test.

-   -   f. When the comparison is complete, for each HME having at least        one source chemical key from the HME Database that matches a key        representing a chemical found on the Observed Materials List        (i.e., at least one flagged bin), calculate that HME's        percentage score—the point score of that HME divided by the        total number of HME bins of source chemicals in that HME's        formulation, multiplied by 100.    -   For example, the formulation for a simple binary explosive HME        has just two bins, so if a match to an OML chemical is found in        each of the two bins, that HME's score is 100%. If a different        HME has four bins, each with one primary and any number of        secondary source chemicals, and the comparison shows that the        Observed Materials List includes matches to the primary source        chemical in one bin, to at least one secondary source chemical        each from two more of the bins, and to no source chemical from        the fourth bin, its percentage score would be ((1+1+1 bins)/4        bins)×100%=75%.

An additional, optional, speed enhancement to the matching process canbe achieved when, in a particularly advantageous embodiment illustratedin FIG. 2, the keys representing the Observed Materials List arearranged in descending order, and the list of keys in the HME Databaseare arranged in ascending order within each HME formulation, and thecomputer is programmed to start the search with the highest key on theObserved Materials List, and search each HME formulation in the HMEDatabase, is where the keys for each HME are arranged in ascending orderwithin that HME, for matches in descending order, against the OML'sdescending order list of keys. In this manner, since the keys match forthe same chemical on each list, once the computer has completedsearching the HME Database, matching the largest Observed Materials Listkey, and then ceasing searching the (now flagged) HME bin in which thematch was found, each subsequent search for a match within an HMEformulation to the next, always smaller, Observed Materials List keyneed proceed only as far down the HME key list for that HME as the keybelonging to the last HME key for which a match was previously found, orthe first HME key that is less than the OML key, whichever isencountered first, and then stop.

In FIG. 2, in searching for the first OML chemical, key 5, the computerstarts at the first HME key in this formulation, on the right, at key 1.Iterating down toward the last HME key in this formulation, key 10, wefind a match at key 5. This key 5 now becomes the bottom of the HME keylist for this search, since, due to the inverted order of the lists, thenext key on the OML cannot be any further down the HME list than key 5.Thus the bottom HME key to be searched moves up as the computer movesdown through the list, in descending order of OML keys. This reduces thenumber of iterations required to search the HME key list for matcheswith the OML keys, saving computational steps.

This inverted-list searching method, combined with terminating thesearch for an Observed Materials List key as soon as it is found to beabsent from the inclusion List, ceasing to search further within a binonce a match to an OML key has been found in that bin, and skippingsearching any HME bin that has already been flagged as providing a matchto any OML key, greatly speeds the search process, and allows it to beperformed on a significantly less powerful digital computer, e.g., apalmtop computer, or even an enabled cellular telephone (though it can,of course, also be run on a more powerful desktop or laptop computer).As previously noted, FIG. 1 illustrates the operation of the searchmethod with all of those performance enhancements in place—the blocksshown in dotted lines are those which represent these optionalperformance enhancing features. As noted above, FIG. 1( a) illustratesthe search in simplest form, in which none of those computer performanceenhancing features have been added.

-   -   g. Calculate a weighted percentage score, or relative        likelihood, of the respective possible HME formulations        (reflecting the reality that a would-be bomb maker would        typically not, for example, have additional HME source chemicals        present if the intent was only to make a very simple formulation        like the binary explosive ANFO), by weighting each HME's        percentage score by determining the HME with the largest total        number of HME bins having keys matching an OML key, i.e., that        with the largest point score, calculated as described above, and        then normalizing the percentage scores as follows:        Weighted percentage score (relative likelihood) of each        HME=(Percentage score of that HME)×((Point score of that        HME)/(Maximum point score achieved by any HME under        evaluation)).

6. Rank the HMEs having any matches to the Observed Materials List bytheir resulting weighted percentage scores, or relative likelihoods,with that having the highest weighted percentage ranking number one, thesecond highest number two, and so on until all the HMEs having anysource chemicals matched to chemicals on the Observed Materials List areaccounted for, and list those HMEs in rank order.

7. Once the above analysis is completed, communicate the results, interms of weighted percentages and relative ranking of the possible HMEformulations, to the user via the provided user output device. Thisdevice would typically be a readable computer screen, though any methodof communicating the results to the user could be substituted for, orused in addition to, such a screen readout.

The results can be reported in any convenient form. In one embodiment,the display would simply constitute a list of the HMEs found to havesome matches, with their respective weighted percentage values,preferably in descending order of weighted percentage scores. In oneadvantageous embodiment, the display would take the form of a bar graph,visually highlighting the relative weighted percentage values for therespective HMEs as well as listing them, with the highest weightedpercentage score at the top, with the longest bar, the second highestnext, with the second longest bar, and so on. Also, while the displaycould simply show all HMEs having any matches at all with the ObservedMaterials List, in one advantageous embodiment the display would belimited to some preset number of the HMEs with the highest weightedpercentage scores, in order to quickly focus the user on the most likelyHME formulations. In another embodiment, the number of HMEs shown would,for the same reason, only include those whose weighted percentage scoresexceeded some set value. FIG. 3 is a screen shot of a typical useroutput device screen, showing the results of applying the instant methodto a sample set of Observed Materials, while running on a currentgeneration smartphone. Here the OML chemicals are sulfuric acid,benzonitrile, nitric acid, cyanogens, and liquid glycol ethers. In theillustration, number 1 indicates that the user has selected to displayHME formulation outcome results. Number 2 displays that the user hashighlighted specific chemicals of interest, for inclusion on theObserved Materials List (OML). Number 3 indicates that the user hasselected to view the probable outcomes, i.e., the most likely HMEformulations corresponding to the OML inputs. Other options includeviewing all possible HME formulations corresponding to these OMLchemicals, or viewing a list of the key components of a given HMEformulation. Number 4 displays the output of the algorithm that modelswhich specific formulations are the most likely intended HMEs to be madefrom the selected set of chemicals. Here, the display shows the severalmost likely HME formulation outcomes corresponding to these sourcechemicals, which are picric acid, nitroglycerin, nitrocellulose, HMTD,and the most likely, with a weighted percentage score (relativelikelihood) of 100%, is an explosive known as EGDN.

8. Repeat the preceding steps 3-7, first resetting all flags on HMEbins, then re-searching for matches between OML keys and HME keys asdescribed above, then recalculating the point scores, percentage scores,weighted percentage scores, and relative likelihood rankings, of theindicated HME formulations as described above, and immediatelydisplaying the new results on the user output device, each time that anew chemical from the Chemicals Database is added to the ObservedMaterials List, or one of the previously entered OML entries is deleted.

This process of recalculating the point scores, percentage scores,weighted percentage scores, and likelihood rankings of HME formulations,and displaying the new results in “real time,” permits the user to “homein on” suspected HME formulations, by selectively adding new items tothe Observed Materials List, to more quickly confirm that a suspectedHME is a likely formulation, or to perform “what if” tests to test theviability of various formulation hypotheses by adding or subtractingeither real observed source chemical species, or hypothetical sourcechemicals that the user judges may clarify whether a particularsuspected HME formulation is a likely outcome.

While the invention has been described in relation to the embodimentsshown in the accompanying Drawing figures, other embodiments,alternatives, and modifications will be apparent to those skilled in theart. It is intended that the Specification be exemplary only, and thatthe true scope and spirit of the invention be indicated by the followingclaims.

I claim:
 1. A system for predicting formulation outcomes for Substancesof Concern (SOC) that correspond to a list of possible source chemicals,comprising: (a) a programmable platform including means for receiving alist of possible source chemicals and storing said list in a platformmemory; (b) a SOC Formulations Database stored in a platform memory; (c)programmable platform means for comparing the list of possible sourcechemicals with the SOC Formulations Database and for predicting thepercentage score or relative likelihood of respective SOC formulationoutcomes; and (d) programmable platform means for reporting saidoutcomes to a user.
 2. A system for predicting chemical formulationoutcomes for one or more Substances of Concern (SOC), comprising adigital computer, with required resources of processing devices, memorystorage, and user input and output devices, and programmed with computerinstructions, to carry out the following steps: a. storing a SOCFormulations Database, containing source chemicals for various SOCformulations, with each chemical uniquely identified; b. storing aChemicals Database, in which chemicals that are also source chemicals inthe SOC Formulations Database have the same identifiers as in the SOCDatabase; c. receiving an Observed Materials List (OML), consisting ofchemicals selected for analysis of their relationships to the SOCformulations; d. comparing the OML chemicals to the source chemicals inthe SOC formulations, calculating a percentage score or relativelikelihood score for each SOC formulation based on the number of matchesbetween that SOC's source chemicals and the OML chemicals; and e.displaying the scores or a graphical representation thereof of those SOCformulations on the user output device.
 3. The system of claim 2,wherein each formulation in the SOC Formulations Database has its sourcechemicals divided into bins, with each bin containing one primary sourcechemical and any number of secondary source chemicals capable of beingused, either directly or with limited preliminary synthesis, in lieu ofthat primary source chemical for that SOC, and wherein each SOCformulation receives a percentage score calculated on the basis of thenumber of bins in that formulation in which at least one primary orsecondary source chemical matches an OML chemical.
 4. The system ofclaim 3, wherein, after calculating the percentage scores of the variousSOC formulations having at least one match to the OML chemicals, thesystem also calculates a weighted percentage score for each such SOCformulation by normalizing that SOC's percentage score against that ofthe SOC formulation with the highest point score, producing a relativelikelihood of each SOC formulation corresponding to the OML chemicals,then ranks those SOC formulations in order of their relativelikelihoods, and displays the weighted percentage scores and rankings ofthose formulations, or a graphical representation thereof, on the useroutput device.
 5. The system of claim 4, wherein, each time that achemical is either added to or deleted from the Observed Materials List,the scores, relative likelihoods, and rankings of the identified SOCformulations are automatically recalculated, and the new resultsdisplayed on the user output device.
 6. The system of claim 2, wherein,after calculating the respective scores of the various SOC formulationshaving matches to the OML chemicals, the system also ranks thoseformulations in order of their scores, and displays the scores andrankings of those formulations, or a graphical representation thereof,on the user output device.
 7. The system of claim 2, wherein chemicalsare placed on the OML by a user entering them via the user input device.8. The system of claim 2, wherein chemicals are placed on the OML by auser selecting them from the Chemicals Database via the user inputdevice.
 9. The system of claim 8, wherein the Chemicals Database alsocontains, associated with each uniquely identified chemical, additionalor alternative names and identifiers by which that chemical is commonlyknown, including, where each exists, its trade names, CAS Registry®number, UN ID number, RTECS number, and phonetic spellings of bothchemical names and trade names, such that a user's selection of any suchalternative name or identifier from the Chemicals Database results inthe addition of the corresponding uniquely identified chemical to theObserved Materials List.
 10. The system of claim 8, wherein theChemicals Database also incorporates hazardous materials and otherSubstances of Concern (SOC), to permit their selection from theChemicals Database for purposes of carrying out other processesprogrammed on the same digital computer, assessing the significance ofthe presence of said hazardous materials and SOCs themselves,independent of whether such materials are also source materials for anySOC formulations.
 11. The system of claim 2, wherein the user inputdevice is a chemical sensor, which analyzes and identifies a sample of achemical, and inserts the chemical's identification directly into theObserved Materials List.
 12. The system of claim 2, wherein theChemicals Database contains primary and secondary source chemicals fromthe SOC Formulation Database.
 13. The system of claim 2, wherein, eachtime that a chemical is either added to or deleted from the ObservedMaterials List, the scores of the identified SOC formulations areautomatically recalculated, and the new results displayed on the useroutput device.
 14. The system of claim 2, wherein the Substances ofConcern (SOC) consist of one or more formulations of substances ofconcern, including but not limited to Home Made Explosives (HME),Chemical Warfare Agents (CW), and Illicit Drugs (ID).
 15. The system ofclaim 2, wherein the Substances of Concern (SOC) consist of one or moreformulations of Home Made Explosives (HME), Chemical Warfare Agents(CW), and Illicit Drugs (ID).
 16. The system of claim 2, wherein theprovided user output device comprises a display on which the output isin the form of a graphical representation, visually highlighting therelative scores for any SOCs found to have matches to any OML chemicals.17. The system of claim 2, in which the digital computer, with requiredresources of processing devices, memory storage, and user input andoutput devices, and programmed with computer instructions for performingthe specified steps, takes the form of a handheld computational device.18. The system of claim 2, wherein each formulation in the SOCFormulations Database has its source chemicals divided into bins, witheach bin containing one primary source chemical and any number ofsecondary source chemicals capable of being used, either directly orwith limited preliminary synthesis, in lieu of that primary sourcechemical for that SOC, and wherein each SOC formulation receives apercentage score calculated on the basis of the number of bins in thatformulation in which at least one primary or secondary source chemicalmatches an OML chemical; and wherein chemicals can be placed on the OMLin one or more of the following ways: by a user entering them via theuser input device, by a user selecting them from the Chemicals Databasevia the user input device, by a user utilizing a user input device whichis a chemical sensor, and which analyzes and identifies a sample of achemical and inserts the chemical's identification directly into theObserved Materials List; and wherein, after calculating the percentagescores of the various SOC formulations having at least one match to theOML chemicals, the system also calculates a weighted percentage scorefor each such SOC formulation by normalizing that SOC's percentage scoreagainst that of the SOC formulation with the highest point score,producing a relative likelihood of each SOC formulation corresponding tothe OML chemicals, then ranks those SOC formulations in order of theirrelative likelihoods, and displays the weighted percentage scores andrankings of those formulations, or a graphical representation thereof,on the user output device; and wherein, each time that a chemical iseither added to or deleted from the Observed Materials List, the scores,relative likelihoods, and rankings of the identified SOC formulationsare automatically recalculated, and the new results displayed on theuser output device.
 19. The system of claim 18, wherein the ChemicalsDatabase also contains, associated with each uniquely identifiedchemical, additional or alternative names and identifiers by which thatchemical is commonly known, including, where each exists, its tradenames, CAS Registry® number, UN ID number, RTECS number, and phoneticspellings of both chemical names and trade names, such that a user'sselection of any such additional or alternative name or identifier fromthe Chemicals Database results in the addition of the correspondinguniquely identified chemical to the Observed Materials List; and whereinthe digital computer, with required resources of processing devices,memory storage, and user input and output devices, and programmed withcomputer instructions for performing the specified steps, takes the formof a handheld computational device.
 20. A method for predictingformulation outcomes for Substances of Concern (SOC) that correspond toa list of possible source chemicals, comprising using: (a) aprogrammable platform including means for receiving a list of possiblesource chemicals and storing said list in a platform memory; (b) a SOCFormulations Database stored in a platform memory; (c) programmableplatform means for comparing the list of possible source chemicals withthe SOC Formulations Database and for predicting the percentage score orrelative likelihood of respective SOC formulation outcomes; and (d)programmable platform means for reporting said outcomes to a user.
 21. Amethod for predicting chemical formulation outcomes for one or moreSubstances of Concern (SOC), comprising providing a digital computer,with required resources of processing devices, memory storage, and userinput and output devices, and programmed with computer instructions, andhaving the computer carry out the following steps: a. storing a SOCFormulations Database, containing source chemicals for various SOCformulations, with each chemical uniquely identified; b. storing aChemicals Database, in which chemicals that are also source chemicals inthe SOC Formulations Database have the same identifiers as in the SOCDatabase; c. receiving an Observed Materials List (OML), consisting ofchemicals selected for analysis of their relationships to the SOCformulations; d. comparing the OML chemicals to the source chemicals inthe SOC formulations, calculating a percentage score or relativelikelihood score for each SOC formulation based on the number of matchesbetween that SOC's source chemicals and the OML chemicals, and e.displaying the scores of those SOC formulations, or a graphicalrepresentation thereof, on the user output device.
 22. The method ofclaim 21, wherein each formulation in the SOC Formulations Database hasits source chemicals divided into bins, with each bin containing oneprimary source chemical and any number of secondary source chemicalscapable of being used, either directly or with limited preliminarysynthesis, in lieu of that primary source chemical for that SOC, andwherein each SOC formulation receives a percentage score calculated onthe basis of the number of bins in that formulation in which at leastone primary or secondary source chemical matches an OML chemical. 23.The method of claim 22, wherein, after calculating the percentage scoresof the various SOC formulations having at least one match to the OMLchemicals, the system also calculates a weighted percentage score foreach such SOC formulation by normalizing that SOC's percentage scoreagainst that of the SOC formulation with the highest point score,producing a relative likelihood of each SOC formulation corresponding tothe OML chemicals, then ranks those SOC formulations in order of theirrelative likelihoods, and displays the weighted percentage scores andrankings of those formulations, or a graphical representation thereof,on the user output device.
 24. The method of claim 23, wherein, eachtime that a chemical is either added to or deleted from the ObservedMaterials List, the scores, relative likelihoods, and rankings of theidentified SOC formulations are automatically recalculated, and the newresults displayed on the user output device.
 25. The method of claim 21,wherein, after calculating the respective scores of the various SOCformulations having matches to the OML chemicals, the system also ranksthose formulations in order of their scores, and displays the scores andrankings of those formulations, or a graphical representation thereof,on the user output device.
 26. The method of claim 21, wherein chemicalsare placed on the OML by a user entering them via the user input device.27. The method of claim 21, wherein chemicals are placed on the OML by auser selecting them from the Chemicals Database via the user inputdevice.
 28. The method of claim 21, wherein the user input device is achemical sensor, which analyzes and identifies a sample of a chemical,and inserts the chemical's identification directly into the ObservedMaterials List.
 29. The method of claim 21, wherein the ChemicalsDatabase also contains, associated with each uniquely identifiedchemical, additional or alternative names and identifiers by which thatchemical is commonly known, including, where each exists, its tradenames, CAS Registry® number, UN ID number, RTECS number, and phoneticspellings of both chemical names and trade names, such that a user'sselection of any such additional or alternative name or identifier fromthe Chemicals Database results in the addition of the correspondinguniquely identified chemical to the Observed Materials List.
 30. Themethod of claim 21, wherein the Chemicals Database also incorporateshazardous materials and other Substances of Interest (SOC), to permittheir selection from the Chemicals Database for purposes of carrying outother processes programmed on the same digital computer, assessing thesignificance of the presence of said hazardous materials and SOCsthemselves, independent of whether such materials are also sourcematerials for any SOC formulations.
 31. The method of claim 21, whereineach formulation in the SOC Formulations Database has its sourcechemicals divided into bins, with each bin containing one primary sourcechemical and any number of secondary source chemicals capable of beingused, either directly or with limited preliminary synthesis, in lieu ofthat primary source chemical for that SOC, and wherein each SOCformulation receives a percentage score calculated on the basis of thenumber of bins in that formulation in which at least one primary orsecondary source chemical matches an OML chemical; and wherein chemicalscan be placed on the OML in one or more of the following ways: by a userentering them via the user input device, by a user selecting them fromthe Chemicals Database via the user input device, by a user utilizing auser input device which is a chemical sensor, and which analyzes andidentifies a sample of a chemical and inserts the chemical'sidentification directly into the Observed Materials List; and wherein,after calculating the percentage scores of the various SOC formulationshaving at least one match to the OML chemicals, the system alsocalculates a weighted percentage score for each such SOC formulation bynormalizing that SOC's percentage score against that of the SOCformulation with the highest point score, producing a relativelikelihood of each SOC formulation corresponding to the OML chemicals,then ranks those SOC formulations in order of their relativelikelihoods, and displays the weighted percentage scores and rankings ofthose formulations, or a graphical representation thereof, on the useroutput device; and wherein, each time that a chemical is either added toor deleted from the Observed Materials List, the scores, relativelikelihoods, and rankings of the identified SOC formulations areautomatically recalculated, and the new results displayed on the useroutput device.
 32. The method of claim 31, wherein the ChemicalsDatabase also contains, associated with each uniquely identifiedchemical, additional or alternative names and identifiers by which thatchemical is commonly known, including, where each exists, its tradenames, CAS Registry® number, UN ID number, RTECS number, and phoneticspellings of both chemical names and trade names, such that a user'sselection of any such additional or alternative name or identifier fromthe Chemicals Database results in the addition of the correspondinguniquely identified chemical to the Observed Materials List; and whereinthe digital computer, with required resources of processing devices,memory storage, and user input and output devices, and programmed withcomputer instructions for performing the specified steps, takes the formof a handheld computational device.
 33. A computer program product,stored on non-transitory machine-readable media, the program comprisingmachine executable instructions for predicting chemical formulationoutcomes for one or more Substances of Concern (SOC), that correspond toa list of possible source chemicals, by carrying out, on a digitalcomputer, with required resources of processing devices, memory storage,and user input and output devices, and programmed with computerinstructions, the steps of: a. storing a SOC Formulations Database,containing source chemicals for various SOC formulations, with eachchemical uniquely identified; b. storing a Chemicals Database, in whichchemicals that are also source chemicals in the SOC FormulationsDatabase have the same identifiers as in the SOC Database; c. receivingan Observed Materials List (OML), consisting of chemicals selected foranalysis of their relationships to the SOC formulations; d. comparingthe OML chemicals to the source chemicals in the SOC formulations,calculating a percentage score or relative likelihood for each SOCformulation based on the number of matches between that SOC's sourcechemicals and the OML chemicals, and e. displaying the scores of thoseSOC formulations, or a graphical representation thereof, on the useroutput device.